Integrated surface and subsurface hydrological modelling to support the assessment of the vulnerability of surface water supplies.
Bibliographic record
Abstract
The protection of water resources is a challenging task and requires a detailed understanding of surface water and groundwater dynamics and interactions. Integrated groundwater and surface water models represent useful tools to assess how groundwater affects the quantity and quality of surface water, which is often a source of drinking water. It is the case for the City of Quebec, Canada, where surface water is the only source of drinking water and where water managers must assess its vulnerability to contamination and depletion. This work focuses on the Nelson River catchment (70 km2), located within the larger catchment of the main drinking water source in Quebec City. The objective is to quantify the links between groundwater and surface water with the 3D integrated hydrological model HydroGeoSphere and simulate coupled surface/subsurface water flow and contaminant transfer. The Nelson catchment model has been calibrated to reproduce observed surface discharges and water table level measurements. Coupled surface water and groundwater flow is then simulated over multiple years using daily meteorological data. Output variables such as distributed infiltration, preferential flow pathways, inter-seasonal changes of surface water volumes, unsaturated and saturated groundwater volumes, are analysed to assess the link between surface water and groundwater. Since this urban area is undergoing growing urbanisation, future scenarios of urban development are also simulated to evaluate the impact of soil sealing on surface/groundwater interactions. The understanding of surface/subsurface interactions in this particular context aims at assessing the vulnerability of the surface drinking water source.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".